Triple
T14869238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zurich city cinemas |
E349698
|
entity |
| Predicate | hasScreeningFormat |
P34185
|
FINISHED |
| Object | feature films |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: feature films | Statement: [Zurich city cinemas, hasScreeningFormat, feature films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScreeningFormat Context triple: [Zurich city cinemas, hasScreeningFormat, feature films]
-
A.
usesFilmFormat
Indicates that one entity employs or is recorded in a particular film format associated with the other entity.
-
B.
appearsInFilmFormat
Indicates that something is presented or occurs within a specific film format or medium of cinematic presentation.
-
C.
hasTheatricalForm
Indicates that something is associated with or presented in a particular theatrical form or style.
-
D.
hasProductionFormat
chosen
Indicates that an entity is associated with, or presented in, a particular production or media format.
-
E.
hasScreenType
Indicates the specific kind or category of screen associated with or used by an entity.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5776b848190bfe3a06ff261dc31 |
completed | April 15, 2026, 12:01 a.m. |
| PD | Predicate disambiguation | batch_69de8c1798c08190b433e9ad21e41a42 |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:55 a.m.